Real-time ridesharing operations for on-demand capacitated systems considering dynamic travel time information
Urban mobility is facing a paradigm shift towards providing more convenient, environmentally friendly, and on-demand services. Satisfying customer needs in a cost-efficient way has been the goal of many ridesharing systems. Capacitated ridesharing is considered as an effective service for reducing traffic congestion and pollution nowadays. Providing more operational strategies that can optimize on-demand ridesharing needs further investigation. In the current work, the authors focus on developing a matching algorithm for solving the on-demand ridesharing operation task in a real-time setting. The authors develop a simulation framework that can be used to propose a real-time shuttle ridesharing search algorithm. The authors propose a novel, computationally efficient, real-time ridesharing algorithm. The authors formulate the ridesharing assignment algorithm as a combinatorial optimization problem. The computational complexity of the proposed algorithm is reduced from exponential to linear, and the search space of the optimization problem is reduced by introducing heuristics. The authors' approach implements dynamic congestion by regularly updating the network’s road segments’ travel time during the simulation horizon to have more realistic results. The authors demonstrate how their algorithm, when applied to the New York City taxi dataset, provides a clear advantage over the current taxi fleet in terms of service rate. Furthermore, the developed simulation framework can provide valuable insights regarding cost functions and operational policies.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
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Supplemental Notes:
- © 2023 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
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Authors:
- Ghandeharioun, Zahra
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0000-0003-1500-5077
- Kouvelas, Anastasios
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0000-0003-4571-2530
- Publication Date: 2023-6
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: 104115
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Serial:
- Transportation Research Part C: Emerging Technologies
- Volume: 151
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 0968-090X
- Serial URL: http://www.sciencedirect.com/science/journal/0968090X
Subject/Index Terms
- TRT Terms: Algorithms; Case studies; Heuristic methods; Optimization; Real time information; Ridesharing; Taxi services; Travel time
- Geographic Terms: New York (New York)
- Subject Areas: Planning and Forecasting; Public Transportation;
Filing Info
- Accession Number: 01890243
- Record Type: Publication
- Files: TRIS
- Created Date: Aug 19 2023 3:06PM